scholarly journals Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Kai Liu ◽  
Tong Wu ◽  
Duane Detwiler ◽  
Jitesh Panchal ◽  
Andres Tovar
Keyword(s):  
Cluster Analysis ◽  
Structural Optimization ◽  
Conceptual Design ◽  
Design Space ◽  
Material Selection ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Cluster Validity Index ◽  
Sizing Optimization ◽  
Expected Improvement Function

Abstract This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.

scholarly journals Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Kai Liu ◽  
Duane Detwiler ◽  
Andres Tovar
Keyword(s):  
Global Optimization ◽  
Conceptual Design ◽  
Optimization Algorithm ◽  
Linear Models ◽  
Learning Algorithm ◽  
Nonlinear Models ◽  
Expected Improvement ◽  
Design Algorithm ◽  
Optimal Material ◽  
Expected Improvement Function

This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness.

scholarly journals Configuration Study of Electric Helicopters for Urban Air Mobility

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 54
Author(s):  
Julia A. Cole ◽  
Lauren Rajauski ◽  
Andrew Loughran ◽  
Alexander Karpowicz ◽  
Stefanie Salinger
Keyword(s):  
Conceptual Design ◽  
Urban Areas ◽  
Design Space ◽  
Design Method ◽  
Urban Air ◽  
Main Rotor ◽  
Potential Benefits ◽  
Compound Helicopter ◽  
Configuration Changes

There is currently interest in the design of small electric vertical take-off and landing aircraft to alleviate ground traffic and congestion in major urban areas. To support progress in this area, a conceptual design method for single-main-rotor and lift-augmented compound electric helicopters has been developed. The design method was used to investigate the feasible design space for electric helicopters based on varying mission profiles and technology assumptions. Within the feasible design space, it was found that a crossover boundary exists as a function of cruise distance and hover time where the most efficient configuration changes from a single-main-rotor helicopter to a lift-augmented compound helicopter. In general, for longer cruise distances and shorter hover times, the lift-augmented compound helicopter is the more efficient configuration. An additional study was conducted to investigate the potential benefits of decoupling the main rotor from the tail rotor. This study showed that decoupling the main rotor and tail rotor has the potential to reduce the total mission energy required in all cases, allowing for increases in mission distances and hover times on the order of 5% for a given battery size.

Aerodynamic Efficiency Optimization of the 1st Stage of Transonic High Pressure Turbine through Lean and Sweep Angles

2019 ◽  
Vol 36 (3) ◽  
pp. 245-256
Author(s):  
Yoonki Kim ◽  
Sanga Lee ◽  
Kwanjung Yee ◽  
Young-Seok Kang
Keyword(s):  
High Pressure ◽  
Surrogate Model ◽  
Design Space ◽  
Latin Hypercube Sampling ◽  
Expected Improvement ◽  
Total Efficiency ◽  
High Pressure Turbine ◽  
Sweep Angle ◽  
Design Variables ◽  
Optimal Latin Hypercube Sampling

Abstract The purpose of this study is to optimize the 1st stage of the transonic high pressure turbine (HPT) for enhancement of aerodynamic performance. Isentropic total-to-total efficiency is designated as the objective function. Since the isentropic efficiency can be improved through modifying the geometry of vane and rotor blade, lean angle and sweep angle are chosen as design variables, which can effectively alter the blade geometry. The sensitivities of each design variable are investigated by applying lean and sweep angles to the base nozzle and rotor, respectively. The design space is also determined based on the results of the parametric study. For the design of experiment (DoE), Optimal Latin Hypercube sampling is adopted, so that 25 evenly distributed samples are selected on the design space. Sequentially, based on the values from the CFD calculation, Kriging surrogate model is constructed and refined using Expected Improvement (EI). With the converged surrogate model, optimum solution is sought by using the Genetic Algorithm. As a result, the efficiency of optimum turbine 1st stage is increased by 1.07 % point compared to that of the base turbine 1st stage. Also, the blade loading, pressure distribution, static entropy, shock structure, and secondary flow are thoroughly discussed.

Gaussian Process Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling

2021 ◽  
Author(s):  
Arunabha Batabyal ◽  
Sugrim Sagar ◽  
Jian Zhang ◽  
Tejesh Dube ◽  
Xuehui Yang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Phase Field ◽  
Bayesian Optimization ◽  
Expected Improvement ◽  
Neck Size ◽  
Sources Of Uncertainty ◽  
Surface Diffusivity ◽  
Input Parameters ◽  
Optimal Values ◽  
Probability Of Improvement

Abstract A persistent problem in the selective laser sintering process is to maintain the quality of additively manufactured parts, which can be attributed to the various sources of uncertainty. In this work, a two-particle phase-field microstructure model has been analyzed. The sources of uncertainty as the two input parameters were surface diffusivity and inter-particle distance. The response quantity of interest (QOI) was selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005, respectively, while those from Expected Improvement were 23.9893 and 33.9627, respectively. The optimization results from the two different acquisition functions seemed to be in good agreement.

Bayesian Optimization of Equilibrium States in Elastomeric Beams

2021 ◽  
pp. 1-36
Author(s):  
David Yoo ◽  
Nathan Hertlein ◽  
Vincent Chen ◽  
Carson Willey ◽  
Andrew Gillman ◽  
...  
Keyword(s):  
Penalty Method ◽  
Vibration Isolation ◽  
Design Space ◽  
Strong Dependence ◽  
Series Representation ◽  
Building Blocks ◽  
Equilibrium States ◽  
Bayesian Optimization ◽  
Stable States ◽  
Beam Shape

Abstract Architected elastomeric beam networks have great potential for energy absorption, multi-resonant vibration isolation, and multi-bandgap elastic wave control, due to the reconfigurability and programmability of their mechanical buckling instabilities. However, navigating this design space is challenging due to bifurcations between mono- and bistable beam designs, inherent geometric nonlinearities, and the strong dependence of buckling properties on beam geometry. To investigate these challenges, we developed a Bayesian optimization framework to control the equilibrium states of an inclined elastomeric beam, while also tuning the energy to transition between these configurations. Leveraging symmetry to reduce the design space, the beam shape is parameterized using a Fourier series representation. A penalty method is developed to include monostable designs in objective functions with dependencies on bistable features, enabling monostable results to still be incorporated in the Gaussian Process surrogate and contribute to the optimization process. Two objectives are optimized in this study, including the position of the second stable equilibrium configuration and the ratio of output to input energy between the two stable states. A scalarized multi-objective optimization is also carried out to study the trade-off between equilibrium position and the energetics of transition between the stable states. The predicted designs are qualitatively verified through experimental testing. Collectively, the study explores a new parameter space for beam buckling, introduces a penalty method to regularize between mono- and bistable domains and provides a library of beams as building blocks to assemble and analyze in future studies.

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